# CFP: IEEE T-SIPN Special Issue on Distributed Information Processing in Social Networks

IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Information Processing in Social Networks

Over the past few decades, online social networks such as Facebook and Twitter have significantly changed the way people communicate and share information with each other. The opinion and behavior of each individual are heavily influenced through interacting with others. These local interactions lead to many interesting collective phenomena such as herding, consensus, and rumor spreading. At the same time, there is always the danger of mob mentality of following crowds, celebrities, or gurus who might provide misleading or even malicious information. Many efforts have been devoted to investigating the collective behavior in the context of various network topologies and the robustness of social networks in the presence of malicious threats. On the other hand, activities in social networks (clicks, searches, transactions, posts, and tweets) generate a massive amount of decentralized data, which is not only big in size but also complex in terms of its structure. Processing these data requires significant advances in accurate mathematical modeling and computationally efficient algorithm design. Many modern technological systems such as wireless sensor and robot networks are virtually the same as social networks in the sense that the nodes in both networks carry disparate information and communicate with constraints. Thus, investigating social networks will bring insightful principles on the system and algorithmic designs of many engineering networks. An example of such is the implementation of consensus algorithms for coordination and control in robot networks. Additionally, more and more research projects nowadays are data-driven. Social networks are natural sources of massive and diverse big data, which present unique opportunities and challenges to further develop theoretical data processing toolsets and investigate novel applications. This special issue aims to focus on addressing distributed information (signal, data, etc.) processing problems in social networks and also invites submissions from all other related disciplines to present comprehensive and diverse perspectives. Topics of interest include, but are not limited to:

• Dynamic social networks: time varying network topology, edge weights, etc.
• Social learning, distributed decision-making, estimation, and filtering
• Consensus and coordination in multi-agent networks
• Modeling and inference for information diffusion and rumor spreading
• Multi-layered social networks where social interactions take place at different scales or modalities
• Resource allocation, optimization, and control in multi-agent networks
• Modeling and strategic considerations for malicious behavior in networks
• Social media computing and networking
• Data mining, machine learning, and statistical inference frameworks and algorithms for handling big data from social networks
• Data-driven applications: attribution models for marketing and advertising, trend prediction, recommendation systems, crowdsourcing, etc.
• Other topics associated with social networks: graphical modeling, trust, privacy, engineering applications, etc.

Important Dates:

• Manuscript submission due: September 15, 2016
• First review completed: November 1, 2016
• Revised manuscript due: December 15, 2016
• Second review completed: February 1, 2017
• Final manuscript due: March 15, 2017
• Publication: June 1, 2017

Guest Editors:

# ITA Workshop 2012 : Talks

The ITA Workshop finished up today, and I know I promised some blogging, but my willpower to take notes kind of deteriorated during the week. For today I’ll put some pointers to talks I saw today which were interesting. I realize I am heavily blogging about Berkeley folks here, but you know, they were interesting talks!

Nadia Fawaz talked about differential privacy for continuous observations : in this model you see $x_1, x_2, x_3, \ldots$ causally and have to estimate the running sum. She had two modifications, one in which you only want a windowed running sum, say for $W$ past values, and one in which the privacy constraint decays and expires after a window of time $W$, so that values $W$ time steps in the past do not have to be protected at all. This yields some differences in the privacy-utility tradeoff in terms of the accuracy of computing the function.

David Tse gave an interesting talk about sequencing DNA via short reads as a communication problem. I had actually had some thoughts along these lines earlier because I am starting to collaborate with my friend Tony Chiang on some informatics problems around next generation sequencing. David wanted to know how many (noiseless) reads $N$ you need to take of a genome of of length $G$ using reads of length $L$. It turns out that the correct scaling in this model is $L/\log G$. Some scaling results were given in a qualitative way, but I guess the quantitative stuff is being written up still.

Michael Jordan talked about the “big data bootstrap” (paper here). You have $n$ data points, where $n$ is huge. The idea is to subsample a set of size $b$ and then do bootstrap estimates of size $n$ on the subsample. I have to read the paper on this but it sounds fascinating.

Anant Sahai talked about how to look at some decentralized linear control problems as implicitly doing some sort of network coding in the deterministic model. One way to view this is to identify unstable modes in the plant as communicating with each other using the controllers as relays in the network. By structurally massaging the control problem into a canonical form, they can make this translation a bit more formal and can translate results about linear stabilization from the 80s into max-flow min-cut type results for network codes. This is mostly work by Se Yong Park, who really ought to have a more complete webpage.

Paolo Minero talked about controlling a linear plant over a rate-limited communication link whose capacity evolves according to a Markov chain. What are the conditions on the rate to ensure stability? He made a connection to Markov jump linear systems that gives the answer in the scalar case, but the necessary and sufficient conditions in the vector case don’t quite match. I always like seeing these sort of communication and control results, even though I don’t work in this area at all. They’re just cool.

There were three talks on consensus in the morning, which I will only touch on briefly. Behrouz Touri gave a talk about part of his thesis work, which was on the Hegselman-Krause opinion dynamics model. It’s not possible to derive a Lyapunov function for this system, but he found a time-varying Lyapunov function, leading to an analysis of the convergence which has some nice connections to products of random stochastic matrices and other topics. Ali Jadbabaie talked about work with Pooya Molavi on non-Bayesian social learning, which combines local Bayesian updating with DeGroot consensus to do distributed learning of a parameter in a network. He had some new sufficient conditions involving disconnected networks that are similar in flavor to his preprint. José Moura talked about distributed Kalman filtering and other consensus meets sensing (consensing?) problems. The algorithms are similar to ones I’ve been looking at lately, so I will have to dig a bit deeper into the upcoming IT Transactions paper.

Cosma reviewed Networks, Crowds, and Markets by Easley and Kleinberg for the American Scientist. I have had the book for a while and just haven’t gotten around to reading it yet, but I should. Its a weighty tome, perhaps a bit too weighty to take to the beach (or on a plane, or…). Alex Dimakis said he reads a little bit before going to bed at night. That’s a heavy glass of warm milk. A fun quote from the review:

What game theorists somewhat disturbingly call rationality is assumed throughout—in other words, game players are assumed to be hedonistic yet infinitely calculating sociopaths endowed with supernatural computing abilities.

Ah, game theory. I anticipate experiencing the unease Cosma feels about the “realities behind the mathematics.”

MIT wants to teach math writing. I thought I learned how to write math by having my Phase II paper draft doused liberally in red ink by Prof. Kleiman. But this is something else entirely. I think a more important thing is to help those who work in mathematical fields or who use mathematics. Perhaps this will be a resource that engineering graduate students can use to improve their own writing.

The Connected States of America, including an interactive map showing how much people in place A talk to people in place B. Via MeFi.

Since I am moving to Chicago this fall, it’s time to get familiar with the L.

Our Paperwork Explosion – an add for IBM. Very weird. Also, vaguely menacing. I love the music though! AVia MeFi.

A mountain-climber’s axe! A mountain-climber’s axe! CAN’T YOU GET THAT THROUGH YOUR SKULL? (Trotsky dies. Bell.)